Q
Qing Dong
Researcher at University of Kitakyushu
Publications - 24
Citations - 117
Qing Dong is an academic researcher from University of Kitakyushu. The author has contributed to research in topics: Geometric programming & Transistor array. The author has an hindex of 6, co-authored 24 publications receiving 107 citations.
Papers
More filters
Proceedings ArticleDOI
How does partitioning matter for 3D floorplanning
TL;DR: It is argued that cut size is a metric good enough for the wire length optimization of 3D floorplanning and suggest that future research focus on other problems such as thermal effect, signal delay, etc.
Proceedings ArticleDOI
Structured analog circuit design and MOS transistor decomposition for high accuracy applications
TL;DR: This paper proposes a simple framework with transistor array for structured analog layout generation, which involves the transistor decomposition, and generates several layouts for a typical CMOS OPAMP circuit and compares the automatically generated layouts with the manual layouts.
Journal ArticleDOI
Structured Placement with Topological Regularity Evaluation
Qing Dong,Shigetoshi Nakatake +1 more
TL;DR: A new concept of floorplanning and block placement is introduced, called structured placement, and a new simulated annealing framework is proposed, called dual SA, where a constructive feature is conveyed to an SA framework, so that it attains a placement balancing the size of regular structures against the area efficiency.
Journal ArticleDOI
Sparsity Adaptive Estimation of Memory Polynomial Based Models for Power Amplifier Behavioral Modeling
TL;DR: Experimental results show that the RSAMP algorithm can efficiently construct a sparse behavioral model with very few terms, but almost have the same model performance with the full model.
Journal ArticleDOI
A new sparse design framework for broadband power amplifier behavioral modeling and digital predistortion
TL;DR: A new sparse framework for the design of the behavioral model and digital predistorter of a broadband power amplifier (PA) by formulating the Volterra kernel to multidimensional memory polynomial and showing how an estimate of the most significant coefficients may be obtained using a matching pursuit (MPT) algorithm by exploiting the sparsity of the model.